1. Building Before Defining the Data Model
The most common mistake: starting in FlutterFlow before modelling the data in Supabase. You build 8 screens, then realise your data structure doesn't support the features you need. Every screen has to be rebuilt.
The fix: spend day one in Supabase's table editor, not in FlutterFlow. Define every table, every column, every foreign key relationship. Ask: "Can I query all the data I need for each screen from this schema?" If yes, start building. If not, fix the schema first.
Data modelling takes 2–4 hours. Rebuilding screens from a wrong schema takes days.
2. Skipping Row-Level Security
Building a multi-user app without Row-Level Security (RLS) means every user can potentially access every other user's data, a serious security and compliance failure.
The fix: enable RLS on every Supabase table from day one. Write the policies: - Users table: `using (auth.uid() = id)` - Items table: `using (auth.uid() = user_id)` or `using (org_id IN (SELECT org_id FROM memberships WHERE user_id = auth.uid()))`
Test by creating two test users and verifying that user A cannot see user B's data. This test takes 10 minutes and should be done before any other testing.
3. Treating the FlutterFlow Preview as Testing
FlutterFlow's browser preview is convenient but it doesn't represent real device behaviour. We've seen apps that work perfectly in preview but crash on iOS, have broken gestures on Android, or display incorrectly on smaller screens.
The fix: test on physical devices weekly throughout development, not just before launch. Use FlutterFlow's "Test Mode" to get a QR code that installs the app on your device. Test on at least one iPhone and one Android device, with different screen sizes.
4. Building Everything Before Testing With Users
Founders often build a complete app, 15 screens, 8 features, before showing it to a single real user. Then they learn that users want something different and have to redesign half the app.
The fix: build the core workflow (3–4 screens) in week 2 and put it in front of 5 real users. What do they try to do that doesn't exist? What do they find confusing? What do they ignore? This feedback shapes weeks 3–6 and saves significant rework.
5. Ignoring App Store Guidelines During Development
App Store rejection after 8 weeks of development is demoralising and expensive. Common rejection reasons:
- No privacy policy linked in the app and the App Store listing - Missing "Sign in with Apple" option when the app offers Google or Facebook login (Apple requires it) - App that's essentially a website wrapper with no native functionality - Missing data deletion feature (required since 2024) - Payments made outside Apple's in-app purchase system for digital goods
The fix: review Apple's App Store Review Guidelines at the start of the project. Build the privacy policy, account deletion flow, and Sign in with Apple from day one.
6. Not Planning for Offline Behaviour
Mobile users lose connectivity, on the subway, in buildings, travelling. An app that shows a blank screen when offline loses trust.
FlutterFlow with Supabase doesn't support offline-first out of the box. The fix: implement graceful degradation. Cache the last successful data fetch in SharedPreferences. Show a "You're offline, showing cached data" banner. Disable write actions with a clear message. Users understand offline limitations, they don't understand a blank app.
7. Underestimating Post-Launch Maintenance
No-code apps aren't zero-maintenance. FlutterFlow releases updates that occasionally break existing functionality. Supabase deprecates APIs. Apple requires updates to support new iOS versions.
Budget 15–20% of development cost per year for maintenance. This covers: FlutterFlow version upgrades, dependency updates, iOS/Android compatibility fixes, and the inevitable "can we add this small feature" requests from users.
Contracts with clients should include a clear maintenance clause. Projects without it become disputes when the first iOS update breaks the app.
Mistake Deep Dive: The Wrong Data Model
Of all the mistakes we see, a flawed data model causes the most expensive rework. The most common data model mistake is building a flat structure when the product requires relational data. For example: an app for managing client projects stores all project data in a single table with columns for client name, project name, and status. This works for one client per project but fails when a client has multiple contacts, a project has multiple milestones, or a milestone has multiple tasks. Adding these features requires restructuring the entire schema and re-wiring every screen.
The second most common mistake is under-designing the user/organisation relationship. A multi-tenant app where users belong to organisations needs a `users` table, an `organisations` table, and a `memberships` junction table. Trying to model this as a single `users` table with an `org_id` column breaks as soon as a user needs to belong to multiple organisations or an organisation has different permission levels per member.
At App Studio, every project begins with a data modelling workshop before any screen is designed. We draw the entity-relationship diagram, define every foreign key, and validate that we can write the SQL query for every screen's data requirement. This investment of 4–6 hours at the start has saved every client from multi-day rebuilds later.
Mistake Deep Dive: Ignoring Row-Level Security Consequences
Skipping RLS isn't just a security risk, it creates a maintenance burden that compounds over time. Without RLS, every query in your app must include manual user filtering: `where user_id = currentUserId`. Every developer touching the code must remember to add this filter. When someone forgets, and someone always does, a data leak occurs silently, with no error to alert you.
With RLS enabled from day one, the database enforces data isolation automatically. A query without a user filter still returns only the rows the authenticated user is allowed to see, because the RLS policy is applied at the database layer before the results are returned. This means even a misconfigured frontend query cannot expose another user's data.
For European apps, GDPR compliance requires that personal data is isolated between users. RLS is one of the technical controls auditors look for when assessing GDPR compliance. Building without it creates a compliance gap that is expensive to close retroactively, particularly because adding RLS after the app is built requires auditing every existing query to ensure none relied on the absence of row-level filtering.
Mistake Deep Dive: Over-Engineering the First Version
No-code tools enable fast iteration, but founders sometimes use them to build the fully-featured vision rather than the minimum viable product. We've seen first versions with complex role hierarchies, multi-currency support, API integrations for services that don't have users yet, and offline sync for an app that has never had a user leave the office. All of this was built before anyone had signed up.
The over-engineering mistake is especially painful in no-code because complexity in FlutterFlow compounds. Adding a third user role type means auditing every screen for permission logic. Adding multi-currency support means revisiting every data display, input, and calculation. Features added speculatively before validation often need to be removed or redesigned when real users provide feedback.
The discipline required for an MVP is cutting every feature that is not required for the core user workflow to function. At App Studio, we use a "launch sprint" concept: the final sprint before submission contains only bug fixes and App Store compliance work, no new features. Every feature that misses the launch sprint goes on the v1.1 backlog. This ensures launches happen on schedule and teams don't spend an extra four weeks adding features to an app that has zero users yet.
Mistake Deep Dive: Choosing the Wrong Monetisation Model
Mobile app monetisation choices have direct technical implications that are often discovered too late. The two most expensive late discoveries: choosing in-app purchases after building a subscription flow using Stripe's web SDK, and choosing a free-with-ads model without accounting for the SDK weight and privacy implications of ad networks.
Apple requires in-app purchases for digital goods sold in iOS apps. If you plan to sell subscriptions, consumable credits, or premium features, you must use Apple's StoreKit APIs. You cannot link to a web payment page for these purchases (Apple explicitly prohibits this). FlutterFlow supports in-app purchases via the RevenueCat plugin, but the integration adds complexity and requires careful handling of receipt validation via a server-side Edge Function.
For B2B apps where the buyer is a company, not an individual, Apple's IAP rules are more flexible. B2B apps can invoice via external payment systems because the product is sold to an enterprise, not a consumer. If your app has a clear B2B model, companies pay monthly via invoice or Stripe, you can avoid StoreKit entirely. Defining this at the start of the project prevents a painful pivot six weeks into development when someone asks "how do users pay?"
How App Studio Avoids These Mistakes
At App Studio, our project process is designed to catch these failure modes before they cost time or money. Every engagement begins with a two-day discovery phase: data modelling, user journey mapping, App Store compliance review, and monetisation architecture definition. Nothing is built until the data schema is validated and the architecture decisions are documented.
We conduct a security review at the end of every sprint. This includes verifying that RLS policies are active on every new table, testing with two isolated test accounts to confirm data separation, and checking that no API keys or secrets are hardcoded in frontend code.
User testing is scheduled, not optional. After the core workflow is built (typically week 2 or 3), we schedule 5 user tests before continuing development. The results directly inform the next sprint's priorities. Over 50+ app projects, this process has eliminated the "built the wrong thing" failure mode that causes most no-code projects to fail. The discipline of process outweighs the speed advantage of skipping it.